Abstract
Object recognition technologies using PCA(principal component analysis) recognize objects by deciding representative features of objects in the model image, extracting feature vectors from objects in an image and measuring the distance between them and object representation. Given frequent recognition problems associated with the use of point-to-point distance approach, this study adopted the K-Nearest Neighbor technique(class-to-class) in which a group of object models of the same class is used as recognition unit for the images inputted on a continual input image. However, we propose the object recognition technique new PCA analysis method that discriminates an object in database even in the case that the variation of illumination in training images exists. Object recognition algorithm proposed here represents more enhanced recognition rate to change of illumination than existing methods.
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Kim, JM., Yang, HS. (2006). A Study on Object Recognition Technology Using PCA in the Variable Illumination. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_99
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DOI: https://doi.org/10.1007/11811305_99
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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